Supernova Models: snewpy.models

Base Class for Supernova Models

class snewpy.models.SupernovaModel

Base class defining an interface to a supernova model.

abstract get_initial_spectra(t, E, flavors=<enum 'Flavor'>)

Get neutrino spectra at the source.

Parameters

• t (astropy.Quantity) – Time to evaluate initial spectra.

• E (astropy.Quantity or ndarray of astropy.Quantity) – Energies to evaluate the initial spectra.

• flavors (iterable of snewpy.neutrino.Flavor) – Return spectra for these flavors only (default: all)

Returns

initialspectra (dict) – Dictionary of neutrino spectra, keyed by neutrino flavor.

abstract get_time()

Returns

returns array of snapshot times from the simulation

get_transformed_spectra(t, E, flavor_xform)

Get neutrino spectra after applying oscillation.

Parameters

• t (astropy.Quantity) – Time to evaluate initial and oscillated spectra.

• E (astropy.Quantity or ndarray of astropy.Quantity) – Energies to evaluate the initial and oscillated spectra.

• flavor_xform (FlavorTransformation) – An instance from the flavor_transformation module.

Returns

dict – Dictionary of transformed spectra, keyed by neutrino flavor.

Derived Models

These models are derived from the SupernovaModel base class. Functions that override those defined in the base class are only documented below if their list of parameters differs.

You can download neutrino fluxes for each of these models using snewpy.get_models("<model_name>").

A submodule with classes used for supernova model files stored on disk. It assumes models are available in a format usable by the AstroPy unified table reader; see https://docs.astropy.org/en/stable/index.html for details.

Based on the ASTERIA (https://github.com/IceCubeOpenSource/ASTERIA) models developed by Navya Uberoi and Spencer Griswold.

Updated summer 2020 by Jim Kneller & Arkin Worlikar. Subsequent updates provided by the SNEWS team.

class snewpy.models.Analytic3Species(filename)

Allow to generate an analytic model given total luminosity, average energy, and rms or pinch, for each species.

class snewpy.models.Bollig_2016(filename, eos='LS220')

Set up a model based on simulations from Bollig et al. (2016). Models were taken, with permission, from the Garching Supernova Archive.

class snewpy.models.Fornax_2019(filename, cache_flux=False)

Model based 3D simulations from D. Vartanyan, A. Burrows, D. Radice, M. A. Skinner and J. Dolence, MNRAS 482(1):351, 2019. Data available at https://www.astro.princeton.edu/~burrows/nu-emissions.3d/.

get_initial_spectra(t, E, theta, phi, flavors=<enum 'Flavor'>, interpolation='linear')

Get neutrino spectra/luminosity curves before flavor transformation.

Parameters

• t (astropy.Quantity) – Time to evaluate initial spectra.

• E (astropy.Quantity or ndarray of astropy.Quantity) – Energies to evaluate the initial spectra.

• theta (astropy.Quantity) – Zenith angle of the spectral emission.

• phi (astropy.Quantity) – Azimuth angle of the spectral emission.

• flavors (iterable of snewpy.neutrino.Flavor) – Return spectra for these flavors only (default: all)

• interpolation (str) – Scheme to interpolate in spectra (‘nearest’, ‘linear’).

Returns

initialspectra (dict) – Dictionary of model spectra, keyed by neutrino flavor.

class snewpy.models.Fornax_2021(filename)

Model based on axisymmetric simulations from A. Burrows and D. Vartanyan, Nature 589:29, 2021. Data available at https://www.astro.princeton.edu/~burrows/nu-emissions.2d/.

class snewpy.models.Kuroda_2020(filename, eos='LS220', mass=<Quantity 20. solMass>)

Set up a model based on simulations from Kuroda et al. (2020).

class snewpy.models.Nakazato_2013(filename)

Set up a model based on simulations from Nakazato et al., ApJ S 205:2 (2013), ApJ 804:75 (2015), PASJ 73:639 (2021). See also http://asphwww.ph.noda.tus.ac.jp/snn/.

class snewpy.models.OConnor_2013(base, mass=15, eos='LS220')

Set up a model based on the black hole formation simulation in O’Connor & Ott (2013).

class snewpy.models.OConnor_2015(filename, eos='LS220')

Set up a model based on the black hole formation simulation in O’Connor (2015).

class snewpy.models.Sukhbold_2015(filename)

Set up a model based on simulations from Sukhbold et al., ApJ 821:38,2016. Models were shared privately by email.

class snewpy.models.Tamborra_2014(filename, eos='LS220')

Set up a model based on 3D simulations from [Tamborra et al., PRD 90:045032, 2014](https://arxiv.org/abs/1406.0006). Data files are from the Garching Supernova Archive.

class snewpy.models.Walk_2018(filename, eos='LS220')

Set up a model based on SASI-dominated simulations from [Walk et al., PRD 98:123001, 2018](https://arxiv.org/abs/1807.02366). Data files are from the Garching Supernova Archive.

class snewpy.models.Walk_2019(filename, eos='LS220')

Set up a model based on SASI-dominated simulations from [Walk et al., PRD 101:123013, 2019](https://arxiv.org/abs/1910.12971). Data files are from the Garching Supernova Archive.

class snewpy.models.Warren_2020(filename, eos='LS220')

Set up a model based on simulations from Warren et al., ApJ 898:139, 2020. Neutrino fluxes available at https://doi.org/10.5281/zenodo.3667908.

class snewpy.models.Zha_2021(filename, eos='STOS_B145')

Set up a model based on the hadron-quark phse transition models from Zha et al. 2021.

Other Models

class snewpy.models.SNOwGLoBES(tarfilename)

A model that does not inherit from SupernovaModel (yet) and imports a group of SNOwGLoBES files.

get_fluence(t)

Return the fluence at a given time t.

Parameters

t (float) – Time in seconds.

Returns

fluence (dict) – A dictionary giving fluence at time t, keyed by flavor.